Control using Reinforcement Learning. In [1]} the authors use Deep Recurrent Q-Networks [2]} in the scenario of a smart grid. The objective is to develop a pricing strategy to maximize broker's (agents) profits. It is proposed a reward shaping strategy once customers are clustered according to their consumption patterns and managed by their respective sub-brokers. Then, a mechanism for credit assignment was necessary to indicate the contribution of each sub-broker to the global return. Wei and colleagues [3]} proposes a data-driven solution to control HVAC (heating, ventilation and air conditioning) system. The objective is to control the environment temperature by handling numerous disturbances with real-time data. The possible actions are a discrete set of airflow rates divided by building zone. Then, the approach split each zone controlling it by distinct artificial neural networks. Sivakumar et al. [4]} propose a network control strategy that acts asynchronously with the environment. The delay, which authors call by \(\delta \) , corresponds to the policy lookup time when selecting an action. Meanwhile, the network proceeds transmitting data in the interval [t, t + \(\delta \) ]. Thus, the transitions are not only dependent on the state and action at some timestep \(t\)  but also the previous action at \(t - 1\) . In [5]} the problem of congestion control in networks is also addressed, and a testbed was released. In [6]}, the authors applied RL to a flight controller of a stratospheric balloon which must handle a partially observable environment and continuous interaction. Finally, Degrave et al. [7]} use RL for nuclear fusion control, which achieved a variety of plasma configurations.
